org.apache.spark.ml.classification

Class NaiveBayes

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>

org.apache.spark.ml.classification.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, Params, DefaultParamsWritable, Identifiable, MLWritable

public class NaiveBayes
extends ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>
implements DefaultParamsWritable

Naive Bayes Classifiers. It supports both Multinomial NB (http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). The input feature values must be nonnegative.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NaiveBayes()
NaiveBayes(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
NaiveBayes	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
Param<String>	featuresCol() Param for features column name.
static M	fit(Dataset<?> dataset)
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
String	getFeaturesCol()
static String	getLabelCol()
String	getLabelCol()
static String	getModelType()
String	getModelType()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static String	getProbabilityCol()
static String	getRawPredictionCol()
String	getRawPredictionCol()
static double	getSmoothing()
double	getSmoothing()
static double[]	getThresholds()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
Param<String>	labelCol() Param for label column name.
static NaiveBayes	load(String path)
static Param<String>	modelType()
Param<String>	modelType() The model type which is a string (case-sensitive).
static Param<?>[]	params()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static Param<String>	probabilityCol()
static Param<String>	rawPredictionCol()
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
static Learner	setFeaturesCol(String value)
static Learner	setLabelCol(String value)
NaiveBayes	setModelType(String value) Set the model type using a string (case-sensitive).
static Learner	setPredictionCol(String value)
static E	setProbabilityCol(String value)
static E	setRawPredictionCol(String value)
NaiveBayes	setSmoothing(double value) Set the smoothing parameter.
static E	setThresholds(double[] value)
static DoubleParam	smoothing()
DoubleParam	smoothing() The smoothing parameter.
static DoubleArrayParam	thresholds()
static String	toString()
static StructType	transformSchema(StructType schema)
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
static void	validateParams()
static MLWriter	write()

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassifier

setProbabilityCol, setThresholds

Methods inherited from class org.apache.spark.ml.classification.Classifier

setRawPredictionCol

Methods inherited from class org.apache.spark.ml.Predictor

fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Constructor Detail

NaiveBayes

public NaiveBayes(String uid)

NaiveBayes

public NaiveBayes()

Method Detail

load

public static NaiveBayes load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

validateParams

public static void validateParams()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)

labelCol

public static final Param<String> labelCol()

getLabelCol

public static final String getLabelCol()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

setLabelCol

public static Learner setLabelCol(String value)

setFeaturesCol

public static Learner setFeaturesCol(String value)

setPredictionCol

public static Learner setPredictionCol(String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

rawPredictionCol

public static final Param<String> rawPredictionCol()

getRawPredictionCol

public static final String getRawPredictionCol()

setRawPredictionCol

public static E setRawPredictionCol(String value)

probabilityCol

public static final Param<String> probabilityCol()

getProbabilityCol

public static final String getProbabilityCol()

thresholds

public static final DoubleArrayParam thresholds()

getThresholds

public static double[] getThresholds()

setProbabilityCol

public static E setProbabilityCol(String value)

setThresholds

public static E setThresholds(double[] value)

smoothing

public static final DoubleParam smoothing()

getSmoothing

public static final double getSmoothing()

modelType

public static final Param<String> modelType()

getModelType

public static final String getModelType()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

write

public static MLWriter write()

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setSmoothing

public NaiveBayes setSmoothing(double value)

Set the smoothing parameter. Default is 1.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setModelType

public NaiveBayes setModelType(String value)

Set the model type using a string (case-sensitive). Supported options: "multinomial" and "bernoulli". Default is "multinomial"

Parameters:

value - (undocumented)

Returns:

(undocumented)

copy

public NaiveBayes copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,NaiveBayes,NaiveBayesModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

smoothing

public DoubleParam smoothing()

The smoothing parameter. (default = 1.0).

Returns:

(undocumented)

getSmoothing

public double getSmoothing()

modelType

public Param<String> modelType()

The model type which is a string (case-sensitive). Supported options: "multinomial" and "bernoulli". (default = multinomial)

Returns:

(undocumented)

getModelType

public String getModelType()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

rawPredictionCol

public Param<String> rawPredictionCol()

Param for raw prediction (a.k.a. confidence) column name.

Returns:

(undocumented)

getRawPredictionCol

public String getRawPredictionCol()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public String getLabelCol()

featuresCol

public Param<String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public String getFeaturesCol()

predictionCol

public Param<String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public String getPredictionCol()